'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__UpperCAmelCase					="\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__UpperCAmelCase					="\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__UpperCAmelCase					="\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:

    def remove_articles(UpperCamelCase__							):
        __lowerCamelCase            =    re.compile(r'''\b(a|an|the)\b'''							,    re.UNICODE							)
        return re.sub(UpperCamelCase__							,    ''' '''							,    UpperCamelCase__							)

    def white_space_fix(UpperCamelCase__							):
        return " ".join(text.split()							)

    def remove_punc(UpperCamelCase__							):
        __lowerCamelCase            =    set(string.punctuation							)
        return "".join(ch for ch in text if ch not in exclude							)

    def lower(UpperCamelCase__							):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__							)							)							)							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Dict:
    return int(normalize_answer(UpperCamelCase__							) == normalize_answer(UpperCamelCase__							)							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [any(compute_exact(UpperCamelCase__							,    UpperCamelCase__							) for ref in refs							) for pred, refs in zip(UpperCamelCase__							,    UpperCamelCase__							)]
    return (sum(UpperCamelCase__							) / len(UpperCamelCase__							)) * 1_00






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    [rgram for rgrams in rgramslist for rgram in rgrams]
    __lowerCamelCase            =    Counter(UpperCamelCase__							)

    __lowerCamelCase            =    Counter(UpperCamelCase__							)
    __lowerCamelCase            =    Counter()
    for sgram, scount in sgramcounter.items():
        __lowerCamelCase            =    scount * numref

    __lowerCamelCase            =    Counter(UpperCamelCase__							)
    __lowerCamelCase            =    Counter()
    for cgram, ccount in cgramcounter.items():
        __lowerCamelCase            =    ccount * numref

    # KEEP
    __lowerCamelCase            =    sgramcounter_rep & cgramcounter_rep
    __lowerCamelCase            =    keepgramcounter_rep & rgramcounter
    __lowerCamelCase            =    sgramcounter_rep & rgramcounter

    __lowerCamelCase            =    0
    __lowerCamelCase            =    0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    __lowerCamelCase            =    1
    __lowerCamelCase            =    1
    if len(UpperCamelCase__							) > 0:
        __lowerCamelCase            =    keeptmpscorea / len(UpperCamelCase__							)
    if len(UpperCamelCase__							) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        __lowerCamelCase            =    keeptmpscorea / sum(keepgramcounterall_rep.values()							)
    __lowerCamelCase            =    0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __lowerCamelCase            =    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __lowerCamelCase            =    sgramcounter_rep - cgramcounter_rep
    __lowerCamelCase            =    delgramcounter_rep - rgramcounter
    __lowerCamelCase            =    sgramcounter_rep - rgramcounter
    __lowerCamelCase            =    0
    __lowerCamelCase            =    0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowerCamelCase            =    1
    if len(UpperCamelCase__							) > 0:
        __lowerCamelCase            =    deltmpscorea / len(UpperCamelCase__							)

    # ADDITION
    __lowerCamelCase            =    set(UpperCamelCase__							) - set(UpperCamelCase__							)
    __lowerCamelCase            =    set(UpperCamelCase__							) & set(UpperCamelCase__							)
    __lowerCamelCase            =    set(UpperCamelCase__							) - set(UpperCamelCase__							)

    __lowerCamelCase            =    0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    __lowerCamelCase            =    1
    __lowerCamelCase            =    1
    if len(UpperCamelCase__							) > 0:
        __lowerCamelCase            =    addtmpscore / len(UpperCamelCase__							)
    if len(UpperCamelCase__							) > 0:
        __lowerCamelCase            =    addtmpscore / len(UpperCamelCase__							)
    __lowerCamelCase            =    0
    if addscore_precision > 0 or addscore_recall > 0:
        __lowerCamelCase            =    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    len(UpperCamelCase__							)

    __lowerCamelCase            =    ssent.split(''' '''							)
    __lowerCamelCase            =    csent.split(''' '''							)
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []

    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    for rsent in rsents:
        __lowerCamelCase            =    rsent.split(''' '''							)
        __lowerCamelCase            =    []
        __lowerCamelCase            =    []
        __lowerCamelCase            =    []
        ragramslist.append(UpperCamelCase__							)
        for i in range(0							,    len(UpperCamelCase__							) - 1							):
            if i < len(UpperCamelCase__							) - 1:
                __lowerCamelCase            =    ragrams[i] + ''' ''' + ragrams[i + 1]
                ragrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 2:
                __lowerCamelCase            =    ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                ragrams.append(UpperCamelCase__							)
            if i < len(UpperCamelCase__							) - 3:
                __lowerCamelCase            =    ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                ragrams.append(UpperCamelCase__							)
        ragramslist.append(UpperCamelCase__							)
        ragramslist.append(UpperCamelCase__							)
        ragramslist.append(UpperCamelCase__							)

    for i in range(0							,    len(UpperCamelCase__							) - 1							):
        if i < len(UpperCamelCase__							) - 1:
            __lowerCamelCase            =    sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(UpperCamelCase__							)
        if i < len(UpperCamelCase__							) - 2:
            __lowerCamelCase            =    sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(UpperCamelCase__							)
        if i < len(UpperCamelCase__							) - 3:
            __lowerCamelCase            =    sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(UpperCamelCase__							)

    for i in range(0							,    len(UpperCamelCase__							) - 1							):
        if i < len(UpperCamelCase__							) - 1:
            __lowerCamelCase            =    cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(UpperCamelCase__							)
        if i < len(UpperCamelCase__							) - 2:
            __lowerCamelCase            =    cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(UpperCamelCase__							)
        if i < len(UpperCamelCase__							) - 3:
            __lowerCamelCase            =    cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(UpperCamelCase__							)

    ((__lowerCamelCase)      ,    (__lowerCamelCase)      ,    (__lowerCamelCase))            =    SARIngram(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    ((__lowerCamelCase)      ,    (__lowerCamelCase)      ,    (__lowerCamelCase))            =    SARIngram(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    ((__lowerCamelCase)      ,    (__lowerCamelCase)      ,    (__lowerCamelCase))            =    SARIngram(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    ((__lowerCamelCase)      ,    (__lowerCamelCase)      ,    (__lowerCamelCase))            =    SARIngram(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    __lowerCamelCase            =    sum([keepascore, keepascore, keepascore, keepascore]							) / 4
    __lowerCamelCase            =    sum([delascore, delascore, delascore, delascore]							) / 4
    __lowerCamelCase            =    sum([addascore, addascore, addascore, addascore]							) / 4
    __lowerCamelCase            =    (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__ = True							,    UpperCamelCase__ = "13a"							,    UpperCamelCase__ = True							)      ->       Tuple:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        __lowerCamelCase            =    sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__							).major >= 2:
            __lowerCamelCase            =    sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__							)()(UpperCamelCase__							)
        else:
            __lowerCamelCase            =    sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__							)
    elif tokenizer == "moses":
        __lowerCamelCase            =    sacremoses.MosesTokenizer().tokenize(UpperCamelCase__							,    return_str=UpperCamelCase__							,    escape=UpperCamelCase__							)
    elif tokenizer == "penn":
        __lowerCamelCase            =    sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__							,    return_str=UpperCamelCase__							)
    else:
        __lowerCamelCase            =    sentence

    if not return_str:
        __lowerCamelCase            =    normalized_sent.split()

    return normalized_sent






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    if not (len(UpperCamelCase__							) == len(UpperCamelCase__							) == len(UpperCamelCase__							)):
        raise ValueError('''Sources length must match predictions and references lengths.'''							)
    __lowerCamelCase            =    0
    for src, pred, refs in zip(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							):
        sari_score += SARIsent(normalize(UpperCamelCase__							)							,    normalize(UpperCamelCase__							)							,    [normalize(UpperCamelCase__							) for sent in refs]							)
    __lowerCamelCase            =    sari_score / len(UpperCamelCase__							)
    return 1_00 * sari_score






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__="exp"							,    UpperCamelCase__=None							,    UpperCamelCase__=False							,    UpperCamelCase__=False							,    UpperCamelCase__=False							,    )      ->       Optional[int]:
    __lowerCamelCase            =    len(references[0]							)
    if any(len(UpperCamelCase__							) != references_per_prediction for refs in references							):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
    __lowerCamelCase            =    [[refs[i] for refs in references] for i in range(UpperCamelCase__							)]
    __lowerCamelCase            =    sacrebleu.corpus_bleu(
        UpperCamelCase__							,    UpperCamelCase__							,    smooth_method=UpperCamelCase__							,    smooth_value=UpperCamelCase__							,    force=UpperCamelCase__							,    lowercase=UpperCamelCase__							,    use_effective_order=UpperCamelCase__							,    )
    return output.score




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,  _KWARGS_DESCRIPTION					)
class        a__ (  datasets.Metric					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION    ,							citation=_CITATION    ,							inputs_description=_KWARGS_DESCRIPTION    ,							features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''    ,							id='''sequence'''       ),
                     '''references''': datasets.Sequence(datasets.Value('''string'''    ,							id='''sequence'''       )    ,							id='''references'''       ),
                 }       )    ,							codebase_urls=[
                 '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                 '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                 '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                 '''https://github.com/mjpost/sacreBLEU''',
             ]    ,							reference_urls=[
                 '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                 '''https://github.com/mjpost/sacreBLEU''',
                 '''https://en.wikipedia.org/wiki/BLEU''',
                 '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
             ]    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Optional[Any]    ,							a						:						str    ,							a						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    {}
         result.update({'''sari''': compute_sari(sources=a    ,							predictions=a    ,							references=a       )}       )
         result.update({'''sacrebleu''': compute_sacrebleu(predictions=a    ,							references=a       )}       )
         result.update({'''exact''': compute_em(predictions=a    ,							references=a       )}       )
         return result

'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
